The two synonymous terms “Plant Pathometry” or “Phytopathometry” were first coined by Large (1953, 1966). Phytopathometry as defined by Nutter et al. (1991) equates with “disease assessment” and is the branch of the discipline of Phytopathology that deals with estimation or measurement of the amount of plant disease (broadly encompassing detection, identification and quantification). Large (1953) stated “At this point what we have been calling ‘disease measurement’ or ‘disease assessment’ enters a new phase. It becomes a systematized and specialized method of mensuration with a derived superstructure. In short, it becomes a branch of plant pathological science, and I think it deserves a better name. The name that I would now, and hereby, propose for it is ‘Plant Pathometry’, from pathos, disease or suffering, and metron, measure.” In the article, Large provides examples (including late blight of potato and choke of cocksfoot) where the new science of phytopathometry was a basis for obtaining valuable information from plant disease surveys. Thus, phytopathometry may be considered the branch of the discipline of phytopathology concerned with detection, identification, and quantification of disease symptoms, or signs of a pathogen.

Large (1966) stated, and Gregory (1982) reiterated five requirements of disease measurement in relation to yield loss analyses:

1. A description of the morphology and development of the healthy crop;
2. Study of the course of the disease on plants in the field;
3. Preparing standard area diagrams for detailed assessment of disease intensity, followed later by a simplified field assessment key (or field key) for field use (a field key was defined as an aid for the rapid visual assessment of a leaf disease on whole plants or plots, or specific sampling areas (Large 1966));
4. Conducting field trials over a number of years, assessing disease progress using a field key for the disease assessments, and recording yields of plots with uncontrolled infection compared with plots kept free from disease;
5. Use of the disease progress curves to select particular assessment points (host growth stages) that will define severity in relation to loss of yield.

Most certainly requirements 1 to 3 are foundational to phytopathometry (the healthy state must be known, ranges in disease symptoms understood, and diagrams, field keys or other methods developed to aid assessments). However, requirements 4 and 5 are related to the analysis of the disease assessment results specifically in relation to crop loss assessment. Disease intensity data are used for epidemiological studies and are the basis for deriving yield loss models. The tactical view of phytopathometry described by Large (1966) and reiterated by Gregory (1982) could be considered an expanded view as it encompasses yield loss. However, Large (1966) also states “Disease measurement is often regarded as a synonym for ‘estimation of losses,’ but this is misleading.” And further states “The main purpose of work on disease measurement is to improve all plant disease recording or reporting, by making it not only qualitative but also much more quantitative.”

Furthermore, plant disease quantification is vital for a myriad of reasons, including: understanding the impact of disease on yield, breeding for plant disease resistance, evaluating and comparing disease control methods, understanding coevolution of plant and pathogen populations, and studying disease epidemiology and pathogen ecology (Madden et al. 2007; Bock et al. 2010; Bock et al. 2016). It underpins all activities within our discipline and extends into related ones, such as agronomy, horticulture, and plant breeding.

Sensu lato, phytopathometry might include measurement of the quantity of the pathogen in the host using molecular methods (but not, for example, measurements of the pathogen propagules in the air or soil), but we have chosen not to include molecular methods in this glossary, instead choosing to restrict the glossary to the more traditional definition of disease measurement based on symptoms or visible pathogen structures. Recently, a range of automated sensor-based, digital technologies have been developed and are being adapted for use in phytopathometry (Bock et al. 2020; Mahlein 2016). Nonetheless, many of the same terms and concepts are identical and appropriate regardless of the methods used. “Plant disease assessment”, or phytopathometry is generally a section (within a chapter on another topic) in plant pathology textbooks as it is a key method used in many plant pathological studies (Agrios 2005; Lucas 2002; Tronsmo et al. 2020).

Since ca. 1970, phytopathometry has been closely associated with botanical epidemiology given its strong quantitative component, and thus phytopathometry has been a branch of plant pathology typically, but not exclusively, studied by plant disease epidemiologists. As with textbooks on plant pathology, plant disease assessment is commonly a chapter or section within reference or teaching texts on plant disease epidemiology (Cooke et al. 2006; Madden et al. 2007). Although epidemiologists have been at the forefront of the research related to phytopathometry, particularly related to development and evaluation of methods, phytopathometry is, as noted, critical to many other plant pathology-related areas of science. Based on the history of phytopathometry, its application, and the statements quoted from Large (1966) we contend that phytopathometry is concerned with the science of the measurement of plant disease. But phytopathometry is not subservient to the branches of epidemiology or yield loss, although it provides a critical service to these and other branches of plant pathology, as well as other disciplines. Thus, we aim to formulate this glossary to embrace phytopathometry as a distinct branch of plant pathology as Large (1953) suggested, and that relates to all applications where disease is quantified (Fig. 1). We therefore envision phytopathometry at the overlapping intersection of plant pathology and measurement science but interfacing closely with three other research disciplines: imaging and sensing technology, psychophysics, statistics, and being an important resource to other disciplines that interface with phytopathology (Fig. 1).

Fig. 1 Inter-relation of various disciplines comprising phytopathometry, at the intersection of plant pathology, measurement science, psychophysics, imaging and sensor technology (including artificial intelligence and robotics), and statistics.

Disease quantification has historically been performed visually or by means of an instrument; hence the use of the two terms ‘estimate’ (visual) or ‘measurement’ (instrument or sensor based). The significant advances in both imaging and remote sensing technologies in the last two decades have directly impacted phytopathometry and its associated terms and concepts. Sensing technologies and image analysis have made substantial advances, and artificial intelligence has recently opened new horizons for phytopathometry, especially for obtaining objective measures of the quantity of plant disease. It has been 20 years since there was a glossary of terms and concepts used in phytopathometry (Nutter 2001), which was based on a similar sentinel work 10 years earlier (Nutter et al. 1991). The 1991 glossary was proposed after the first phase of quantitative research on plant disease assessment (1970 to 1990). It also presented concepts and terms from the perspective of quantifying crop loss and did not consider the many other purposes of disease quantification which are also critical. Thus, included herein are new terms (with respect to those defined in Nutter et al (1991)), including many from imaging, sensing technology and artificial intelligence, and more broadly, statistics and measurement science. We have also refined definitions or updated our understanding of some terms as used in plant disease assessment (Everitt 1991; Madden et al. 2007; Bock et al. 2010, 2016 and 2020; Chiang et al. 2020). A call for an enhanced dialogue between plant pathologists and remote sensing researchers was made recently (Heim et al. 2019), which is justified given the explosion in the number of applications of remote sensing and artificial intelligence to detect or measure plant diseases in the last five years (Bock et al. 2020). Indeed, the path to success in sensor-based phytopathometry is transdisciplinary research among plant pathologists, electrical engineers, agronomists and informatics specialists (Mahlein 2016).

Before providing the updated and revised list of concepts and definitions, there are two terms that need to be clearly defined, with rationale, as they are so pivotal to phytopathometry and the practice of plant pathology. The first is “disease measurement”. Historically this has referred to visual estimates of plant disease (Large 1966). However, with the advent of sensor-based technology, disease can now be actually and accurately measured based on pixels or wave bands with healthy or diseased characteristics. Thus, we contend that disease measurement refers to those assessments made only using sensors – and may refer to the number of individuals diseased, counts of a disease symptom on an organ, or the proportional quantity of disease on an individual specimen. In contrast, a “disease estimate” is one obtained through visual disease assessment, most commonly referring to estimates of the proportional quantity of disease on a specimen (but could be count-based data). The second term requiring discussion is “disease severity”. Here, we define disease severity as the “degree to which a specimen (plant or plant part) is diseased. Severity may be described quantitatively on a scale or as a proportion of the unit area diseased (commonly a percentage), the number of lesions present, or a ranked numeric order of descriptions of symptoms in a progression from mild to severe (as applicable for many systemic diseases)” (Fig. 2).

Fig. 2 The updated concept and definition of plant disease severity to encompass the original definition (percentage area diseased, Nutter et al. 1991), and the metrics of lesion counts or density, and ordinal scale based measurements using classes based on ether intervals of the percentage scale or descriptions of symptoms as defined in this article.

This definition differs from most prior definitions that described severity purely on the basis of area diseased – but many plant diseases show degrees of severity without an easily defined area affected (as noted, many systemic diseases). Thus, severity is broadened to be applicable to almost all plant diseases where it can be rated quantitatively using ordinal or ratio scales, and not solely in the narrow sense of proportion (or percentage) of area diseased as defined by Nutter et al. (1991). Precedent also encourages the definition of severity to be revisited and broadened, to provide a terminus communis to metrics of disease quantification on a specimen. Thus, Seem (1984) referred to disease density (i.e. lesions per leaf) as a form of severity, although McRoberts et al. (2003) acknowledged this was not the intended definition of Nutter et al. (1991), and decided to use density as a separate measure. We assert that lesion counts (or density) are indeed one of the metrics of severity. A perusal of the plant pathology literature will demonstrate that many authors already use these various metrics as measures of severity as described in numerous journal articles. For example, Pethybridge et al. (2020) and Cowling and Gilchrist (1980) referred to lesion counts as disease severity – the latter authors also used lesion size as a measure of severity. Quantitative ordinal scales based on the percentage scale to determine quantity of disease are well known and widely used, with the stated purpose of rating disease severity (Horsfall and Barratt 1945; Kousik et al. 2018; Urea and Haverson 2014). Similarly, qualitative, or descriptive ordinal scales are used for many diseases where diseased area estimates are not possible, including many systemic and viral diseases – in the literature the rating has often been described as a disease severity metric (Gottwald et al. 1989; Ling and Scott 2007; Pascual et al. 2010; Cook et al. 2020; Wang et al. 2020). And there are many examples of the percentage scale being used to estimate disease severity, in accordance with the original and narrow Nutter et al. (1991) definition (Colson et al. 2003; Scherm et al. 2009; Bock et al. 2017). The fact that so many studies have used the term “severity” to describe the full range of metrics quantifying disease on an individual specimen suggests that the terms needs redefining to allow plant pathologists to describe the quantity of disease on a specimen generically as “disease severity” without having to resort to semantic gymnastics. In addition, a review of the definitions for disease intensity, prevalence, and incidence (all of which remain unchanged) will augment the rationale for the case (for the relationships among these see Fig. 3). We argue that broadening the term “disease severity” to include all metrics that can be used to rate a disease quantitatively using a numeric scale has many practical advantages. Finally, many terms here are defined specifically as they are used in phytopathometry, and in some cases (e.g. incidence) may have different meanings in other disciplines.

Fig. 3 The relationships between plant disease intensity, prevalence, incidence, and severity based on the concepts and definitions in this article.

The terms and concepts defined below are from a broad range of sources, but most notably they stem from those initially defined by Nutter et al. (1991), and from Nutter (2001), D’Arcy et al. (2001), Madden et al. (2007), Bock et al. (2010) and Bock et al (2016). Other sentinel references are included (Everitt 1991; Nutter et al. 2006; McRoberts et al. 2003; Behmann et al. 2015; Del Ponte et al. 2017; Paulus and Mahlein 2020; Paulus 2019). The Special Topic article of Nutter et al. (1991) was the outcome of a subcommittee that was appointed by the then Plant Disease Losses Committee of the American Phytopathological Society. The list below is compiled by the authors without specific societal authority, but with the common purpose of bringing together in a single source, an updated and comprehensive (as of 2021) list of terms, definitions and concepts currently used or making debuts in phytopathometry. This glossary includes 128 terms, which is an increment compared to the original glossary that had 58 terms, and which included terms and concepts related to yield loss measurement. Finally, usage in the historic literature may vary depending on era and subdiscipline. Therefore, care should be taken when using terms and we encourage their context definition when used in individual studies. We are strong proponents of including operational definitions for technical terms being used in any study so that readers can clearly understand how the term is applied in specific situations.